• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.491-499
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• 2017

Railway turnout systems are one of the most important systems in a railway and abnormal turnout systems can cause serious accidents. To detect an abnormal state of a turnout, turnout clearance detectors are widely used. These devices consider a failure of a turnout clearance detectors to be a failure of the turnout system, that could hinder train operations. Analysis of turnout clearance detector failures is very important to ensure normal train operation. We categorized failures of detectors into four groups to identify failure characteristics of the 140 detectors, which are composed of main line detectors (A), side tracks (B), detectors that are in operation more than 80 times a day (C) and detectors that are in operation fewer than 10 times per day. Failures of detectors have mainly been caused in the control part, in the cables and sensors; failures are classified into four groups (A, B, C and D). We have tried to find failure density distributions for each type of failures, inferring the parameter distributions a priori. Finally, using the Bayesian inference we proposed a maintenance time for control parts through the mean time of the detector, life and the life expectancy.

• Convergence Security Journal
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• v.13 no.1
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• pp.71-78
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• 2013

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The doubly bounded power law distribution model makeup Weibull distribution applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R^2$.

• Convergence Security Journal
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• v.11 no.1
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• pp.25-32
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The Weibull distribution applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R_{sq}$.